Conditional motion predictions

ABSTRACT

Among other things, techniques are described for conditional motion predictions. The techniques include generating, by a planning circuit, a set of candidate trajectories for a vehicle based on possible macro actions by the vehicle, predicting, by the planning circuit and for at least some trajectories in the set of candidate trajectories, a response by a target vehicle to the respective trajectory and a probability of the response by the target vehicle, selecting, by the planning circuit, a trajectory from the set of candidate trajectories based at least in part on the response by the target vehicle to the trajectory, the probability of the response by the target vehicle, and characteristics of the trajectory, and operating, by a control circuit, the vehicle based on the selected trajectory.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patent application Ser. No. 17/003,965, filed Aug. 26, 2020, the entire contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

This description relates to conditional motion predictions for vehicles.

BACKGROUND

A vehicle, such as an autonomous vehicle, can include sensors that produce data regarding objects or persons in an area surrounding the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an autonomous vehicle having autonomous capability.

FIG. 2 illustrates an example “cloud” computing environment.

FIG. 3 illustrates a computer system.

FIG. 4 shows an example architecture for an autonomous vehicle.

FIG. 5 shows an example of inputs and outputs that may be used by a perception module.

FIG. 6 shows an example of a LiDAR system.

FIG. 7 shows the LiDAR system in operation.

FIG. 8 shows the operation of the LiDAR system in additional detail.

FIG. 9 shows a block diagram of the relationships between inputs and outputs of a planning module.

FIG. 10 shows a directed graph used in path planning.

FIG. 11 shows a block diagram of the inputs and outputs of a control module.

FIG. 12 shows a block diagram of the inputs, outputs, and components of a controller.

FIGS. 13-16 show block diagrams of the inputs, outputs, and components of a conditional motion module.

FIG. 17 shows a flowchart of an example process for conditional motion prediction.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.

In the drawings, specific arrangements or orderings of schematic elements, such as those representing devices, modules, instruction blocks and data elements, are shown for ease of description. However, it should be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments.

Further, in the drawings, where connecting elements, such as solid or dashed lines or arrows, are used to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not shown in the drawings so as not to obscure the disclosure. In addition, for ease of illustration, a single connecting element is used to represent multiple connections, relationships or associations between elements. For example, where a connecting element represents a communication of signals, data, or instructions, it should be understood by those skilled in the art that such element represents one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication.

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

Several features are described hereafter that can each be used independently of one another or with any combination of other features. However, any individual feature may not address any of the problems discussed above or might only address one of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Although headings are provided, information related to a particular heading, but not found in the section having that heading, may also be found elsewhere in this description. Embodiments are described herein according to the following outline:

1. General Overview

2. System Overview

3. Autonomous Vehicle Architecture

4. Autonomous Vehicle Inputs

5. Autonomous Vehicle Planning

6. Autonomous Vehicle Control

7. Conditional Motion Predictions

General Overview

A vehicle (such as an autonomous vehicle) can generate a set of candidate trajectories for a maneuver based on actions that are characteristic of the maneuver. For example, to perform a lane change or lane merge maneuver, the vehicle generates a set of candidate trajectories based on actions such as follow lane, change lane, or some combination thereof. Each trajectory in the set of candidate trajectories can be applied to a model to predict how other vehicles will respond (including the probability of such a response) should the vehicle travel along the trajectory. The predicted responses for a trajectory, as well as characteristics of the trajectory itself, can be used to assign a score to each trajectory in the set of candidate trajectories. The vehicle can then select an optimal trajectory (e.g., the highest scoring trajectory) and can operate the vehicle based on the selected trajectory to perform the maneuver.

Some of the advantages of these techniques include improved safety and efficiency in vehicle maneuvers. For example, by accounting for the effect of a trajectory on both the vehicle and other vehicles, a trajectory can be selected that maximizes efficiency and avoids collisions or other unsafe events for each actor. In addition, by limiting the set of candidate trajectories to those that are realistic for a given maneuver, the computational cost of predicting responses and selecting an optimal trajectory is reduced while maintaining high quality results.

System Overview

FIG. 1 shows an example of an autonomous vehicle 100 having autonomous capability.

As used herein, the term “autonomous capability” refers to a function, feature, or facility that enables a vehicle to be partially or fully operated without real-time human intervention, including without limitation fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possesses autonomous capability.

As used herein, “vehicle” includes means of transportation of goods or people. For example, cars, buses, trains, airplanes, drones, trucks, boats, ships, submersibles, dirigibles, etc. A driverless car is an example of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AV from a first spatiotemporal location to second spatiotemporal location. In an embodiment, the first spatiotemporal location is referred to as the initial or starting location and the second spatiotemporal location is referred to as the destination, final location, goal, goal position, or goal location. In some examples, a trajectory is made up of one or more segments (e.g., sections of road) and each segment is made up of one or more blocks (e.g., portions of a lane or intersection). In an embodiment, the spatiotemporal locations correspond to real world locations. For example, the spatiotemporal locations are pick up or drop-off locations to pick up or drop-off persons or goods.

As used herein, “sensor(s)” includes one or more hardware components that detect information about the environment surrounding the sensor. Some of the hardware components can include sensing components (e.g., image sensors, biometric sensors), transmitting and/or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components such as analog-to-digital converters, a data storage device (such as a RAM and/or a nonvolatile storage), software or firmware components and data processing components such as an ASIC (application-specific integrated circuit), a microprocessor and/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list) or data stream that includes one or more classified or labeled objects detected by one or more sensors on the AV vehicle or provided by a source external to the AV.

As used herein, a “road” is a physical area that can be traversed by a vehicle, and may correspond to a named thoroughfare (e.g., city street, interstate freeway, etc.) or may correspond to an unnamed thoroughfare (e.g., a driveway in a house or office building, a section of a parking lot, a section of a vacant lot, a dirt path in a rural area, etc.). Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utility vehicles, etc.) are capable of traversing a variety of physical areas not specifically adapted for vehicle travel, a “road” may be a physical area not formally defined as a thoroughfare by any municipality or other governmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed by a vehicle. A lane is sometimes identified based on lane markings. For example, a lane may correspond to most or all of the space between lane markings, or may correspond to only some (e.g., less than 50%) of the space between lane markings. For example, a road having lane markings spaced far apart might accommodate two or more vehicles between the markings, such that one vehicle can pass the other without traversing the lane markings, and thus could be interpreted as having a lane narrower than the space between the lane markings, or having two lanes between the lane markings. A lane could also be interpreted in the absence of lane markings. For example, a lane may be defined based on physical features of an environment, e.g., rocks and trees along a thoroughfare in a rural area or, e.g., natural obstructions to be avoided in an undeveloped area. A lane could also be interpreted independent of lane markings or physical features. For example, a lane could be interpreted based on an arbitrary path free of obstructions in an area that otherwise lacks features that would be interpreted as lane boundaries. In an example scenario, an AV could interpret a lane through an obstruction-free portion of a field or empty lot. In another example scenario, an AV could interpret a lane through a wide (e.g., wide enough for two or more lanes) road that does not have lane markings. In this scenario, the AV could communicate information about the lane to other AVs so that the other AVs can use the same lane information to coordinate path planning among themselves.

The term “over-the-air (OTA) client” includes any AV, or any electronic device (e.g., computer, controller, IoT device, electronic control unit (ECU)) that is embedded in, coupled to, or in communication with an AV.

The term “over-the-air (OTA) update” means any update, change, deletion or addition to software, firmware, data or configuration settings, or any combination thereof, that is delivered to an OTA client using proprietary and/or standardized wireless communications technology, including but not limited to: cellular mobile communications (e.g., 2G, 3G, 4G, 5G), radio wireless area networks (e.g., WiFi) and/or satellite Internet.

The term “edge node” means one or more edge devices coupled to a network that provide a portal for communication with AVs and can communicate with other edge nodes and a cloud based computing platform, for scheduling and delivering OTA updates to OTA clients.

The term “edge device” means a device that implements an edge node and provides a physical wireless access point (AP) into enterprise or service provider (e.g., VERIZON, AT&T) core networks. Examples of edge devices include but are not limited to: computers, controllers, transmitters, routers, routing switches, integrated access devices (IADs), multiplexers, metropolitan area network (MAN) and wide area network (WAN) access devices.

“One or more” includes a function being performed by one element, a function being performed by more than one element, e.g., in a distributed fashion, several functions being performed by one element, several functions being performed by several elements, or any combination of the above.

It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.

The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this description, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

As used herein, an AV system refers to the AV along with the array of hardware, software, stored data, and data generated in real-time that supports the operation of the AV. In an embodiment, the AV system is incorporated within the AV. In an embodiment, the AV system is spread across several locations. For example, some of the software of the AV system is implemented on a cloud computing environment similar to cloud computing environment 300 described below with respect to FIG. 3 .

In general, this document describes technologies applicable to any vehicles that have one or more autonomous capabilities including fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles, such as so-called Level 5, Level 4 and Level 3 vehicles, respectively (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, which is incorporated by reference in its entirety, for more details on the classification of levels of autonomy in vehicles). The technologies described in this document are also applicable to partially autonomous vehicles and driver assisted vehicles, such as so-called Level 2 and Level 1 vehicles (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems). In an embodiment, one or more of the Level 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicle operations (e.g., steering, braking, and using maps) under certain operating conditions based on processing of sensor inputs. The technologies described in this document can benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.

Autonomous vehicles have advantages over vehicles that require a human driver. One advantage is safety. For example, in 2016, the United States experienced 6 million automobile accidents, 2.4 million injuries, 40,000 fatalities, and 13 million vehicles in crashes, estimated at a societal cost of $910+ billion. U.S. traffic fatalities per 100 million miles traveled have been reduced from about six to about one from 1965 to 2015, in part due to additional safety measures deployed in vehicles. For example, an additional half second of warning that a crash is about to occur is believed to mitigate 60% of front-to-rear crashes. However, passive safety features (e.g., seat belts, airbags) have likely reached their limit in improving this number. Thus, active safety measures, such as automated control of a vehicle, are the likely next step in improving these statistics. Because human drivers are believed to be responsible for a critical pre-crash event in 95% of crashes, automated driving systems are likely to achieve better safety outcomes, e.g., by reliably recognizing and avoiding critical situations better than humans; making better decisions, obeying traffic laws, and predicting future events better than humans; and reliably controlling a vehicle better than a human.

Referring to FIG. 1 , an AV system 120 operates the AV 100 along a trajectory 198 through an environment 190 to a destination 199 (sometimes referred to as a final location) while avoiding objects (e.g., natural obstructions 191, vehicles 193, pedestrians 192, cyclists, and other obstacles) and obeying rules of the road (e.g., rules of operation or driving preferences).

In an embodiment, the AV system 120 includes devices 101 that are instrumented to receive and act on operational commands from the computer processors 146. We use the term “operational command” to mean an executable instruction (or set of instructions) that causes a vehicle to perform an action (e.g., a driving maneuver). Operational commands can, without limitation, including instructions for a vehicle to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate, decelerate, perform a left turn, and perform a right turn. In an embodiment, computing processors 146 are similar to the processor 304 described below in reference to FIG. 3 . Examples of devices 101 include a steering control 102, brakes 103, gears, accelerator pedal or other acceleration control mechanisms, windshield wipers, side-door locks, window controls, and turn-indicators.

In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring properties of state or condition of the AV 100, such as the AV's position, linear and angular velocity and acceleration, and heading (e.g., an orientation of the leading end of AV 100). Example of sensors 121 are GPS, inertial measurement units (IMU) that measure both vehicle linear accelerations and angular rates, wheel speed sensors for measuring or estimating wheel slip ratios, wheel brake pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.

In an embodiment, the sensors 121 also include sensors for sensing or measuring properties of the AV's environment. For example, monocular or stereo video cameras 122 in the visible light, infrared or thermal (or both) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.

In an embodiment, the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121. In an embodiment, the data storage unit 142 is similar to the ROM 308 or storage device 310 described below in relation to FIG. 3 . In an embodiment, memory 144 is similar to the main memory 306 described below. In an embodiment, the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions. In an embodiment, data relating to the environment 190 is transmitted to the AV 100 via a communications channel from a remotely located database 134.

In an embodiment, the AV system 120 includes communications devices 140 for communicating measured or inferred properties of other vehicles' states and conditions, such as positions, linear and angular velocities, linear and angular accelerations, and linear and angular headings to the AV 100. These devices include Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication devices and devices for wireless communications over point-to-point or ad hoc networks or both. In an embodiment, the communications devices 140 communicate across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). A combination of Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication (and, in some embodiments, one or more other types of communication) is sometimes referred to as Vehicle-to-Everything (V2X) communication. V2X communication typically conforms to one or more communications standards for communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices 140 include communication interfaces. For example, wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interfaces. The communication interfaces transmit data from a remotely located database 134 to AV system 120. In an embodiment, the remotely located database 134 is embedded in a cloud computing environment 200 as described in FIG. 2 . The communication interfaces 140 transmit data collected from sensors 121 or other data related to the operation of AV 100 to the remotely located database 134. In an embodiment, communication interfaces 140 transmit information that relates to teleoperations to the AV 100. In some embodiments, the AV 100 communicates with other remote (e.g., “cloud”) servers 136.

In an embodiment, the remotely located database 134 also stores and transmits digital data (e.g., storing data such as road and street locations). Such data is stored on the memory 144 on the AV 100, or transmitted to the AV 100 via a communications channel from the remotely located database 134.

In an embodiment, the remotely located database 134 stores and transmits historical information about driving properties (e.g., speed and acceleration profiles) of vehicles that have previously traveled along trajectory 198 at similar times of day. In one implementation, such data may be stored on the memory 144 on the AV 100, or transmitted to the AV 100 via a communications channel from the remotely located database 134.

Computing devices 146 located on the AV 100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities.

In an embodiment, the AV system 120 includes computer peripherals 132 coupled to computing devices 146 for providing information and alerts to, and receiving input from, a user (e.g., an occupant or a remote user) of the AV 100. In an embodiment, peripherals 132 are similar to the display 312, input device 314, and cursor controller 316 discussed below in reference to FIG. 3 . The coupling is wireless or wired. Any two or more of the interface devices may be integrated into a single device.

In an embodiment, the AV system 120 receives and enforces a privacy level of a passenger, e.g., specified by the passenger or stored in a profile associated with the passenger. The privacy level of the passenger determines how particular information associated with the passenger (e.g., passenger comfort data, biometric data, etc.) is permitted to be used, stored in the passenger profile, and/or stored on the cloud server 136 and associated with the passenger profile. In an embodiment, the privacy level specifies particular information associated with a passenger that is deleted once the ride is completed. In an embodiment, the privacy level specifies particular information associated with a passenger and identifies one or more entities that are authorized to access the information. Examples of specified entities that are authorized to access information can include other AVs, third party AV systems, or any entity that could potentially access the information.

A privacy level of a passenger can be specified at one or more levels of granularity. In an embodiment, a privacy level identifies specific information to be stored or shared. In an embodiment, the privacy level applies to all the information associated with the passenger such that the passenger can specify that none of her personal information is stored or shared. Specification of the entities that are permitted to access particular information can also be specified at various levels of granularity. Various sets of entities that are permitted to access particular information can include, for example, other AVs, cloud servers 136, specific third party AV systems, etc.

In an embodiment, the AV system 120 or the cloud server 136 determines if certain information associated with a passenger can be accessed by the AV 100 or another entity. For example, a third-party AV system that attempts to access passenger input related to a particular spatiotemporal location must obtain authorization, e.g., from the AV system 120 or the cloud server 136, to access the information associated with the passenger. For example, the AV system 120 uses the passenger's specified privacy level to determine whether the passenger input related to the spatiotemporal location can be presented to the third-party AV system, the AV 100, or to another AV. This enables the passenger's privacy level to specify which other entities are allowed to receive data about the passenger's actions or other data associated with the passenger.

FIG. 2 illustrates an example “cloud” computing environment. Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services). In typical cloud computing systems, one or more large cloud data centers house the machines used to deliver the services provided by the cloud. Referring now to FIG. 2 , the cloud computing environment 200 includes cloud data centers 204 a, 204 b, and 204 c that are interconnected through the cloud 202. Data centers 204 a, 204 b, and 204 c provide cloud computing services to computer systems 206 a, 206 b, 206 c, 206 d, 206 e, and 206 f connected to cloud 202.

The cloud computing environment 200 includes one or more cloud data centers. In general, a cloud data center, for example the cloud data center 204 a shown in FIG. 2 , refers to the physical arrangement of servers that make up a cloud, for example the cloud 202 shown in FIG. 2 , or a particular portion of a cloud. For example, servers are physically arranged in the cloud datacenter into rooms, groups, rows, and racks. A cloud datacenter has one or more zones, which include one or more rooms of servers. Each room has one or more rows of servers, and each row includes one or more racks. Each rack includes one or more individual server nodes. In some implementation, servers in zones, rooms, racks, and/or rows are arranged into groups based on physical infrastructure requirements of the datacenter facility, which include power, energy, thermal, heat, and/or other requirements. In an embodiment, the server nodes are similar to the computer system described in FIG. 3 . The data center 204 a has many computing systems distributed through many racks.

The cloud 202 includes cloud data centers 204 a, 204 b, and 204 c along with the network and networking resources (for example, networking equipment, nodes, routers, switches, and networking cables) that interconnect the cloud data centers 204 a, 204 b, and 204 c and help facilitate the computing systems' 206 a-f access to cloud computing services. In an embodiment, the network represents any combination of one or more local networks, wide area networks, or internetworks coupled using wired or wireless links deployed using terrestrial or satellite connections. Data exchanged over the network, is transferred using any number of network layer protocols, such as Internet Protocol (IP), Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay, etc. Furthermore, in embodiments where the network represents a combination of multiple sub-networks, different network layer protocols are used at each of the underlying sub-networks. In some embodiments, the network represents one or more interconnected internetworks, such as the public Internet.

The computing systems 206 a-f or cloud computing services consumers are connected to the cloud 202 through network links and network adapters. In an embodiment, the computing systems 206 a-f are implemented as various computing devices, for example servers, desktops, laptops, tablet, smartphones, Internet of Things (IoT) devices, autonomous vehicles (including, cars, drones, shuttles, trains, buses, etc.) and consumer electronics. In an embodiment, the computing systems 206 a-f are implemented in or as a part of other systems.

FIG. 3 illustrates a computer system 300. In an implementation, the computer system 300 is a special purpose computing device. The special-purpose computing device is hard-wired to perform the techniques or includes digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. In various embodiments, the special-purpose computing devices are desktop computer systems, portable computer systems, handheld devices, network devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.

In an embodiment, the computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled with a bus 302 for processing information. The hardware processor 304 is, for example, a general-purpose microprocessor. The computer system 300 also includes a main memory 306, such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus 302 for storing information and instructions to be executed by processor 304. In one implementation, the main memory 306 is used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 304. Such instructions, when stored in non-transitory storage media accessible to the processor 304, render the computer system 300 into a special-purpose machine that is customized to perform the operations specified in the instructions.

In an embodiment, the computer system 300 further includes a read only memory (ROM) 308 or other static storage device coupled to the bus 302 for storing static information and instructions for the processor 304. A storage device 310, such as a magnetic disk, optical disk, solid-state drive, or three-dimensional cross point memory is provided and coupled to the bus 302 for storing information and instructions.

In an embodiment, the computer system 300 is coupled via the bus 302 to a display 312, such as a cathode ray tube (CRT), a liquid crystal display (LCD), plasma display, light emitting diode (LED) display, or an organic light emitting diode (OLED) display for displaying information to a computer user. An input device 314, including alphanumeric and other keys, is coupled to bus 302 for communicating information and command selections to the processor 304. Another type of user input device is a cursor controller 316, such as a mouse, a trackball, a touch-enabled display, or cursor direction keys for communicating direction information and command selections to the processor 304 and for controlling cursor movement on the display 312. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x-axis) and a second axis (e.g., y-axis), that allows the device to specify positions in a plane.

According to one embodiment, the techniques herein are performed by the computer system 300 in response to the processor 304 executing one or more sequences of one or more instructions contained in the main memory 306. Such instructions are read into the main memory 306 from another storage medium, such as the storage device 310. Execution of the sequences of instructions contained in the main memory 306 causes the processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry is used in place of or in combination with software instructions.

The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media includes non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, solid-state drives, or three-dimensional cross point memory, such as the storage device 310. Volatile media includes dynamic memory, such as the main memory 306. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NV-RAM, or any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 302. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infrared data communications.

In an embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to the processor 304 for execution. For example, the instructions are initially carried on a magnetic disk or solid-state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system 300 receives the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector receives the data carried in the infrared signal and appropriate circuitry places the data on the bus 302. The bus 302 carries the data to the main memory 306, from which processor 304 retrieves and executes the instructions. The instructions received by the main memory 306 may optionally be stored on the storage device 310 either before or after execution by processor 304.

The computer system 300 also includes a communication interface 318 coupled to the bus 302. The communication interface 318 provides a two-way data communication coupling to a network link 320 that is connected to a local network 322. For example, the communication interface 318 is an integrated service digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface 318 is a local area network (LAN) card to provide a data communication connection to a compatible LAN. In some implementations, wireless links are also implemented. In any such implementation, the communication interface 318 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

The network link 320 typically provides data communication through one or more networks to other data devices. For example, the network link 320 provides a connection through the local network 322 to a host computer 324 or to a cloud data center or equipment operated by an Internet Service Provider (ISP) 326. The ISP 326 in turn provides data communication services through the world-wide packet data communication network now commonly referred to as the “Internet” 328. The local network 322 and Internet 328 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 320 and through the communication interface 318, which carry the digital data to and from the computer system 300, are example forms of transmission media. In an embodiment, the network 320 contains the cloud 202 or a part of the cloud 202 described above.

The computer system 300 sends messages and receives data, including program code, through the network(s), the network link 320, and the communication interface 318. In an embodiment, the computer system 300 receives code for processing. The received code is executed by the processor 304 as it is received, and/or stored in storage device 310, or other non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG. 4 shows an example architecture 400 for an autonomous vehicle (e.g., the AV 100 shown in FIG. 1 ). The architecture 400 includes a perception module 402 (sometimes referred to as a perception circuit), a planning module 404 (sometimes referred to as a planning circuit), a control module 406 (sometimes referred to as a control circuit), a localization module 408 (sometimes referred to as a localization circuit), and a database module 410 (sometimes referred to as a database circuit). Each module plays a role in the operation of the AV 100. Together, the modules 402, 404, 406, 408, and 410 may be part of the AV system 120 shown in FIG. 1 . In some embodiments, any of the modules 402, 404, 406, 408, and 410 is a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits [ASICs]), hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these things). Each of the modules 402, 404, 406, 408, and 410 is sometimes referred to as a processing circuit (e.g., computer hardware, computer software, or a combination of the two). A combination of any or all of the modules 402, 404, 406, 408, and 410 is also an example of a processing circuit.

In use, the planning module 404 receives data representing a destination 412 and determines data representing a trajectory 414 (sometimes referred to as a route) that can be traveled by the AV 100 to reach (e.g., arrive at) the destination 412. In order for the planning module 404 to determine the data representing the trajectory 414, the planning module 404 receives data from the perception module 402, the localization module 408, and the database module 410.

The perception module 402 identifies nearby physical objects using one or more sensors 121, e.g., as also shown in FIG. 1 . The objects are classified (e.g., grouped into types such as pedestrian, bicycle, automobile, traffic sign, etc.) and a scene description including the classified objects 416 is provided to the planning module 404.

The planning module 404 also receives data representing the AV position 418 from the localization module 408. The localization module 408 determines the AV position by using data from the sensors 121 and data from the database module 410 (e.g., a geographic data) to calculate a position. For example, the localization module 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization module 408 includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In an embodiment, the high-precision maps are constructed by adding data through automatic or manual annotation to low-precision maps.

The control module 406 receives the data representing the trajectory 414 and the data representing the AV position 418 and operates the control functions 420 a-c (e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the AV 100 to travel the trajectory 414 to the destination 412. For example, if the trajectory 414 includes a left turn, the control module 406 will operate the control functions 420 a-c in a manner such that the steering angle of the steering function will cause the AV 100 to turn left and the throttling and braking will cause the AV 100 to pause and wait for passing pedestrians or vehicles before the turn is made.

Autonomous Vehicle Inputs

FIG. 5 shows an example of inputs 502 a-d (e.g., sensors 121 shown in FIG. 1 ) and outputs 504 a-d (e.g., sensor data) that is used by the perception module 402 (FIG. 4 ). One input 502 a is a LiDAR (Light Detection and Ranging) system (e.g., LiDAR 123 shown in FIG. 1 ). LiDAR is a technology that uses light (e.g., bursts of light such as infrared light) to obtain data about physical objects in its line of sight. A LiDAR system produces LiDAR data as output 504 a. For example, LiDAR data is collections of 3D or 2D points (also known as a point clouds) that are used to construct a representation of the environment 190.

Another input 502 b is a RADAR system. RADAR is a technology that uses radio waves to obtain data about nearby physical objects. RADARs can obtain data about objects not within the line of sight of a LiDAR system. A RADAR system 502 b produces RADAR data as output 504 b. For example, RADAR data are one or more radio frequency electromagnetic signals that are used to construct a representation of the environment 190.

Another input 502 c is a camera system. A camera system uses one or more cameras (e.g., digital cameras using a light sensor such as a charge-coupled device [CCD]) to obtain information about nearby physical objects. A camera system produces camera data as output 504 c. Camera data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). In some examples, the camera system has multiple independent cameras, e.g., for the purpose of stereopsis (stereo vision), which enables the camera system to perceive depth. Although the objects perceived by the camera system are described here as “nearby,” this is relative to the AV. In use, the camera system may be configured to “see” objects far, e.g., up to a kilometer or more ahead of the AV. Accordingly, the camera system may have features such as sensors and lenses that are optimized for perceiving objects that are far away.

Another input 502 d is a traffic light detection (TLD) system. A TLD system uses one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual navigation information. A TLD system produces TLD data as output 504 d. TLD data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). A TLD system differs from a system incorporating a camera in that a TLD system uses a camera with a wide field of view (e.g., using a wide-angle lens or a fish-eye lens) in order to obtain information about as many physical objects providing visual navigation information as possible, so that the AV 100 has access to all relevant navigation information provided by these objects. For example, the viewing angle of the TLD system may be about 120 degrees or more.

In some embodiments, outputs 504 a-d are combined using a sensor fusion technique. Thus, either the individual outputs 504 a-d are provided to other systems of the AV 100 (e.g., provided to a planning module 404 as shown in FIG. 4 ), or the combined output can be provided to the other systems, either in the form of a single combined output or multiple combined outputs of the same type (e.g., using the same combination technique or combining the same outputs or both) or different types type (e.g., using different respective combination techniques or combining different respective outputs or both). In some embodiments, an early fusion technique is used. An early fusion technique is characterized by combining outputs before one or more data processing steps are applied to the combined output. In some embodiments, a late fusion technique is used. A late fusion technique is characterized by combining outputs after one or more data processing steps are applied to the individual outputs.

FIG. 6 shows an example of a LiDAR system 602 (e.g., the input 502 a shown in FIG. 5 ). The LiDAR system 602 emits light 604 a-c from a light emitter 606 (e.g., a laser transmitter). Light emitted by a LiDAR system is typically not in the visible spectrum; for example, infrared light is often used. Some of the light 604 b emitted encounters a physical object 608 (e.g., a vehicle) and reflects back to the LiDAR system 602. (Light emitted from a LiDAR system typically does not penetrate physical objects, e.g., physical objects in solid form.) The LiDAR system 602 also has one or more light detectors 610, which detect the reflected light. In an embodiment, one or more data processing systems associated with the LiDAR system generates an image 612 representing the field of view 614 of the LiDAR system. The image 612 includes information that represents the boundaries 616 of a physical object 608. In this way, the image 612 is used to determine the boundaries 616 of one or more physical objects near an AV.

FIG. 7 shows the LiDAR system 602 in operation. In the scenario shown in this figure, the AV 100 receives both camera system output 504 c in the form of an image 702 and LiDAR system output 504 a in the form of LiDAR data points 704. In use, the data processing systems of the AV 100 compares the image 702 to the data points 704. In particular, a physical object 706 identified in the image 702 is also identified among the data points 704. In this way, the AV 100 perceives the boundaries of the physical object based on the contour and density of the data points 704.

FIG. 8 shows the operation of the LiDAR system 602 in additional detail. As described above, the AV 100 detects the boundary of a physical object based on characteristics of the data points detected by the LiDAR system 602. As shown in FIG. 8 , a flat object, such as the ground 802, will reflect light 804 a-d emitted from a LiDAR system 602 in a consistent manner. Put another way, because the LiDAR system 602 emits light using consistent spacing, the ground 802 will reflect light back to the LiDAR system 602 with the same consistent spacing. As the AV 100 travels over the ground 802, the LiDAR system 602 will continue to detect light reflected by the next valid ground point 806 if nothing is obstructing the road. However, if an object 808 obstructs the road, light 804 e-f emitted by the LiDAR system 602 will be reflected from points 810 a-b in a manner inconsistent with the expected consistent manner. From this information, the AV 100 can determine that the object 808 is present.

Path Planning

FIG. 9 shows a block diagram 900 of the relationships between inputs and outputs of a planning module 404 (e.g., as shown in FIG. 4 ). In general, the output of a planning module 404 is a route 902 from a start point 904 (e.g., source location or initial location), and an end point 906 (e.g., destination or final location). The route 902 is typically defined by one or more segments. For example, a segment is a distance to be traveled over at least a portion of a street, road, highway, driveway, or other physical area appropriate for automobile travel. In some examples, e.g., if the AV 100 is an off-road capable vehicle such as a four-wheel-drive (4WD) or all-wheel-drive (AWD) car, SUV, pick-up truck, or the like, the route 902 includes “off-road” segments such as unpaved paths or open fields.

In addition to the route 902, a planning module also outputs lane-level route planning data 908. The lane-level route planning data 908 is used to traverse segments of the route 902 based on conditions of the segment at a particular time. For example, if the route 902 includes a multi-lane highway, the lane-level route planning data 908 includes trajectory planning data 910 that the AV 100 can use to choose a lane among the multiple lanes, e.g., based on whether an exit is approaching, whether one or more of the lanes have other vehicles, or other factors that vary over the course of a few minutes or less. Similarly, in some implementations, the lane-level route planning data 908 includes speed constraints 912 specific to a segment of the route 902. For example, if the segment includes pedestrians or un-expected traffic, the speed constraints 912 may limit the AV 100 to a travel speed slower than an expected speed, e.g., a speed based on speed limit data for the segment.

In an embodiment, the inputs to the planning module 404 includes database data 914 (e.g., from the database module 410 shown in FIG. 4 ), current location data 916 (e.g., the AV position 418 shown in FIG. 4 ), destination data 918 (e.g., for the destination 412 shown in FIG. 4), and object data 920 (e.g., the classified objects 416 as perceived by the perception module 402 as shown in FIG. 4 ). In some embodiments, the database data 914 includes rules used in planning. Rules are specified using a formal language, e.g., using Boolean logic. In any given situation encountered by the AV 100, at least some of the rules will apply to the situation. A rule applies to a given situation if the rule has conditions that are met based on information available to the AV 100, e.g., information about the surrounding environment. Rules can have priority. For example, a rule that says, “if the road is a freeway, move to the leftmost lane” can have a lower priority than “if the exit is approaching within a mile, move to the rightmost lane.”

FIG. 10 shows a directed graph 1000 used in path planning, e.g., by the planning module 404 (FIG. 4 ). In general, a directed graph 1000 like the one shown in FIG. 10 is used to determine a path between any start point 1002 and end point 1004. In real-world terms, the distance separating the start point 1002 and end point 1004 may be relatively large (e.g., in two different metropolitan areas) or may be relatively small (e.g., two intersections abutting a city block or two lanes of a multi-lane road).

In an embodiment, the directed graph 1000 has nodes 1006 a-d representing different locations between the start point 1002 and the end point 1004 that could be occupied by an AV 100. In some examples, e.g., when the start point 1002 and end point 1004 represent different metropolitan areas, the nodes 1006 a-d represent segments of roads. In some examples, e.g., when the start point 1002 and the end point 1004 represent different locations on the same road, the nodes 1006 a-d represent different positions on that road. In this way, the directed graph 1000 includes information at varying levels of granularity. In an embodiment, a directed graph having high granularity is also a subgraph of another directed graph having a larger scale. For example, a directed graph in which the start point 1002 and the end point 1004 are far away (e.g., many miles apart) has most of its information at a low granularity and is based on stored data, but also includes some high granularity information for the portion of the graph that represents physical locations in the field of view of the AV 100.

The nodes 1006 a-d are distinct from objects 1008 a-b which cannot overlap with a node. In an embodiment, when granularity is low, the objects 1008 a-b represent regions that cannot be traversed by automobile, e.g., areas that have no streets or roads. When granularity is high, the objects 1008 a-b represent physical objects in the field of view of the AV 100, e.g., other automobiles, pedestrians, or other entities with which the AV 100 cannot share physical space. In an embodiment, some or all of the objects 1008 a-b are a static objects (e.g., an object that does not change position such as a street lamp or utility pole) or dynamic objects (e.g., an object that is capable of changing position such as a pedestrian or other car).

The nodes 1006 a-d are connected by edges 1010 a-c. If two nodes 1006 a-b are connected by an edge 1010 a, it is possible for an AV 100 to travel between one node 1006 a and the other node 1006 b, e.g., without having to travel to an intermediate node before arriving at the other node 1006 b. (When we refer to an AV 100 traveling between nodes, we mean that the AV 100 travels between the two physical positions represented by the respective nodes.) The edges 1010 a-c are often bidirectional, in the sense that an AV 100 travels from a first node to a second node, or from the second node to the first node. In an embodiment, edges 1010 a-c are unidirectional, in the sense that an AV 100 can travel from a first node to a second node, however the AV 100 cannot travel from the second node to the first node. Edges 1010 a-c are unidirectional when they represent, for example, one-way streets, individual lanes of a street, road, or highway, or other features that can only be traversed in one direction due to legal or physical constraints.

In an embodiment, the planning module 404 uses the directed graph 1000 to identify a path 1012 made up of nodes and edges between the start point 1002 and end point 1004.

An edge 1010 a-c has an associated cost 1014 a-b. The cost 1014 a-b is a value that represents the resources that will be expended if the AV 100 chooses that edge. A typical resource is time. For example, if one edge 1010 a represents a physical distance that is twice that as another edge 1010 b, then the associated cost 1014 a of the first edge 1010 a may be twice the associated cost 1014 b of the second edge 1010 b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges 1010 a-b may represent the same physical distance, but one edge 1010 a may require more fuel than another edge 1010 b, e.g., because of road conditions, expected weather, etc.

When the planning module 404 identifies a path 1012 between the start point 1002 and end point 1004, the planning module 404 typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.

Autonomous Vehicle Control

FIG. 11 shows a block diagram 1100 of the inputs and outputs of a control module 406 (e.g., as shown in FIG. 4 ). A control module operates in accordance with a controller 1102 which includes, for example, one or more processors (e.g., one or more computer processors such as microprocessors or microcontrollers or both) similar to processor 304, short-term and/or long-term data storage (e.g., memory random-access memory or flash memory or both) similar to main memory 306, ROM 308, and storage device 310, and instructions stored in memory that carry out operations of the controller 1102 when the instructions are executed (e.g., by the one or more processors).

In an embodiment, the controller 1102 receives data representing a desired output 1104. The desired output 1104 typically includes a velocity, e.g., a speed and a heading. The desired output 1104 can be based on, for example, data received from a planning module 404 (e.g., as shown in FIG. 4 ). In accordance with the desired output 1104, the controller 1102 produces data usable as a throttle input 1106 and a steering input 1108. The throttle input 1106 represents the magnitude in which to engage the throttle (e.g., acceleration control) of an AV 100, e.g., by engaging the steering pedal, or engaging another throttle control, to achieve the desired output 1104. In some examples, the throttle input 1106 also includes data usable to engage the brake (e.g., deceleration control) of the AV 100. The steering input 1108 represents a steering angle, e.g., the angle at which the steering control (e.g., steering wheel, steering angle actuator, or other functionality for controlling steering angle) of the AV should be positioned to achieve the desired output 1104.

In an embodiment, the controller 1102 receives feedback that is used in adjusting the inputs provided to the throttle and steering. For example, if the AV 100 encounters a disturbance 1110, such as a hill, the measured speed 1112 of the AV 100 is lowered below the desired output speed. In an embodiment, any measured output 1114 is provided to the controller 1102 so that the necessary adjustments are performed, e.g., based on the differential 1113 between the measured speed and desired output. The measured output 1114 includes measured position 1116, measured velocity 1118, (including speed and heading), measured acceleration 1120, and other outputs measurable by sensors of the AV 100.

In an embodiment, information about the disturbance 1110 is detected in advance, e.g., by a sensor such as a camera or LiDAR sensor, and provided to a predictive feedback module 1122. The predictive feedback module 1122 then provides information to the controller 1102 that the controller 1102 can use to adjust accordingly. For example, if the sensors of the AV 100 detect (“see”) a hill, this information can be used by the controller 1102 to prepare to engage the throttle at the appropriate time to avoid significant deceleration.

FIG. 12 shows a block diagram 1200 of the inputs, outputs, and components of the controller 1102. The controller 1102 has a speed profiler 1202 which affects the operation of a throttle/brake controller 1204. For example, the speed profiler 1202 instructs the throttle/brake controller 1204 to engage acceleration or engage deceleration using the throttle/brake 1206 depending on, e.g., feedback received by the controller 1102 and processed by the speed profiler 1202.

The controller 1102 also has a lateral tracking controller 1208 which affects the operation of a steering controller 1210. For example, the lateral tracking controller 1208 instructs the steering controller 1210 to adjust the position of the steering angle actuator 1212 depending on, e.g., feedback received by the controller 1102 and processed by the lateral tracking controller 1208.

The controller 1102 receives several inputs used to determine how to control the throttle/brake 1206 and steering angle actuator 1212. A planning module 404 provides information used by the controller 1102, for example, to choose a heading when the AV 100 begins operation and to determine which road segment to traverse when the AV 100 reaches an intersection. A localization module 408 provides information to the controller 1102 describing the current location of the AV 100, for example, so that the controller 1102 can determine if the AV 100 is at a location expected based on the manner in which the throttle/brake 1206 and steering angle actuator 1212 are being controlled. In an embodiment, the controller 1102 receives information from other inputs 1214, e.g., information received from databases, computer networks, etc.

Conditional Motion Predictions

FIG. 13 shows a block diagram 1300 of the inputs, outputs, and components of a conditional motion module 1302. In general, the conditional motion module 1302 simulates a set of candidate motion trajectories for a vehicle (e.g., the AV 100 shown in FIG. 1 ) to predict a response to each trajectory by one or more other vehicles in the environment (e.g., the environment 190). For example, the conditional motion module 1302 simulates a set of candidate trajectories for the vehicle in response to an indication (e.g., from the planning module 404) that the vehicle is to perform a maneuver (e.g., a lane change, a merge, a turn, etc.), at predefined intervals (e.g., time steps), or both, among others. The conditional motion module 1302 then selects a trajectory for the vehicle based on features of the trajectory and the predicted response(s) to the trajectory. In this manner, the conditional motion module 1302 plans the motion of the vehicle in a way that is optimal for both the vehicle and other vehicles in the environment.

As shown in FIG. 13 , the conditional motion module 1302 includes a candidate trajectory generator 1304 to generate a set of candidate motion trajectories for a vehicle, a conditional predictor 1306 to predict a likely response by another vehicle (sometimes referred to as a “target vehicle”) to a respective candidate trajectory, and a trajectory selector 1308 to select a trajectory from the set of candidate trajectories for the vehicle based on, for example, the predicted response by the target vehicle to the candidate trajectory and characteristics of the candidate trajectory, among other information. The conditional motion module 1304 and its components (e.g., the candidate trajectory generator 1304, conditional predictor 1306, and trajectory selector 1308) can be part of a vehicle system (e.g., the AV system 120), such as part of a planning module (e.g., the planning module 404) of the vehicle system. In an embodiment, the conditional motion module 1304 and its components are implemented by one or more processors (e.g., one or more computer processors such as microprocessors or microcontrollers or both) similar to processor 304, short-term and/or long-term data storage (e.g., memory random-access memory or flash memory or both) similar to main memory 306, ROM 308, and storage device 310, and instructions stored in memory that carry out operations of the respective component when the instructions are executed (e.g., by the one or more processors). In an embodiment, the trajectory generator 1304 generates a set of candidate trajectories for the vehicle at predefined intervals (e.g., time steps), in response to a determination that the vehicle is to perform a maneuver (e.g., a lane change, a merge, a turn, etc.), or both, among others.

In general, the trajectory generator 1304 generates a set of candidate trajectories for the vehicle. Each candidate trajectory represents a path or route that can be traveled by the vehicle from an initial location (e.g., a start or current location) toward a destination location (e.g., an end or goal location). In an embodiment, each candidate trajectory includes at least one feature or parameter that distinguishes it from the other candidate trajectories in the set of candidate trajectories. For example, a candidate trajectory has a different initial location or destination location, or both, than some or all of the other candidate trajectories. As another example, a candidate trajectory has the same initial location or destination location, or both, as some or all of the other candidate trajectories, but has a different speed profile, lateral motion profile (e.g., lateral motion within or among lanes), or other motion parameter than some or all of the other candidate trajectories. In an embodiment, the set of candidate trajectories make up a segment of an overall trajectory or route (e.g., route 902) between a starting location (e.g., starting point 904) and a destination location (e.g., end point 906) for the vehicle.

To generate the set of candidate trajectories, the candidate trajectory generator 1304 receives vehicle data 1310 and environment data 1312. The vehicle data 1310 represents information about the vehicle, including the current location of the vehicle (e.g., the vehicle position 418 received from the localization module 408), the destination of the vehicle (e.g., the destination 412), information about a state or condition of the vehicle (e.g., the vehicle's velocity, acceleration, heading, etc.), and information about properties or capabilities of the vehicle (e.g., speed or acceleration profiles for the vehicle), among others. The environment data 1312 represents information about the vehicle's environment, including information about vehicles or other objects within the environment (e.g., the classified objects 416 as perceived by the perception module 402), map data for the environment (e.g., high-definition map data received from the database module 410), and rules for planning or operation within the environment (e.g., received from the database module 410), among others. Each of the vehicle data 1310 and environment data 1312 are received from one or more sensors at the vehicle (e.g., sensors 121), one or more modules of the vehicle (e.g., modules 402, 404, 406, 408), one or more databases communicatively coupled with the vehicle (e.g., the database 134), or other sources in communication with the vehicle (e.g., through V2X communications with other vehicles or infrastructure), or combinations of them.

Referring to FIG. 14 , the candidate trajectory generator 1304 uses the vehicle data 1310 and the environment data 1312 to generate a set of candidate trajectories 1400 for the vehicle. For example, given information about the vehicle (e.g., current position, destination, velocity, etc.) and the vehicle's environment (e.g., map data and object data), the candidate trajectory generator 1304 generates a set of candidate trajectories 1400 that can be traveled by the vehicle toward its destination.

In an embodiment, the trajectory generator 1304 generates the set of candidate trajectories 1400 using one or more predefined behaviors or macro actions 1402 for the vehicle. We use the term “macro action” broadly to refer to a predefined action or sequence of actions that can be performed by the vehicle. For example, the macro actions 1402 include a first macro action defined by a follow lane action, a second macro action defined by a sequence of actions including (1) change lane and (2) follow lane, and a third macro action defined by a sequence of actions including (1) follow lane, (2) change lane, and (3) follow lane, among others. The macro actions 1402 can correspond to a maneuver to be performed by the vehicle or can be applicable to multiple maneuvers.

In an embodiment, the macro actions 1402 are parameterized using one or more parameters 1404. For example, a macro action 1402 defined by a sequence of actions including (1) follow lane, (2) change lane, and (3) follow lane follow lane is parameterized such that the follow lane action is parameterized by a velocity parameter 1404, and the change lane action is parameterized by a time parameter 1404 for timing the lane change. In an embodiment, each parameter 1404 includes a set of possible values selected based on properties of the vehicle or its environment, or both. For example, the velocity parameter 1404 includes a set of possible velocity values selected such the vehicle is capable of traveling at the velocity (e.g., based on the vehicle's current velocity and acceleration/deceleration capabilities), or the velocity is appropriate for the vehicle's environment (e.g., within the speed limit), or both.

To generate the set of candidate trajectories 1400, the candidate trajectory generator 1304 applies the vehicle data 1310 and the environment data 1312 to the macro actions 1402 and the parameters 1404. For example, the candidate trajectory generator 1304 initializes the parameters 1404 to include a set of possible values based on the vehicle data 1310 or the environment data 1312, or both. The candidate trajectory generator 1304 then iterates some or all of the macro actions 1402 for the set of parameter values 1404 to generate the set of candidate trajectories 1400 from the vehicle's current location toward its destination location. For example, for a macro action 1402 defined by a follow lane action and parameterized by a velocity parameter 1404, the candidate trajectory generator 1304 generates a set of candidate trajectories 1400 that can be traveled by the vehicle to follow the lane from its current position at different velocities. By generating a limited set of candidate trajectories 1400 based on macro actions 1402 and parameters 1404 that are possible for the vehicle, the number of candidate trajectories is reduced relative to implementations which generate trajectories without such constraints. This in turn reduces the amount of resources consumed to generate trajectories and predict responses while maintaining coverage of most driving behaviors.

Referring to FIG. 15 , the set of candidate trajectories 1400 generated by the trajectory generator 1304 are provided to the conditional motion predictor 1306. In general, the conditional motion predictor 1306 predicts responses 1500 (including a probability of each response) by target vehicles to each candidate trajectory 1400. To do so, the conditional motion predictor 1306 receives scene data 1502 and target vehicle data 1504 in addition to the candidate trajectories 1400 received from the trajectory generator 1304. The scene data 1502 includes high-definition map data and context data (e.g., surroundings, motion history, etc.) for each target vehicle (e.g., the target vehicles as perceived by the perception module 402 and included in the environment data 1312). In an embodiment, the scene data 1502 for each target vehicle is rasterized as a bird's-eye view image (e.g., a RGB image). The target vehicle data 1504 includes state or condition information for each target vehicle, such as position, velocity, acceleration, heading, or yaw rate, or combinations of them, among others.

To predict the responses 1500 by a target vehicle to a particular candidate trajectory 1400, each of the scene data 1502, the target vehicle data 1504, and data representing the candidate trajectory 1400 (e.g., a rasterized bird's-eye view image of the candidate trajectory) is input into a model 1506. In an embodiment, the model 1506 is a deep learning model (e.g., a neural network model, such as a convolutional neural network [CNN]) trained to output a multimodal probabilistic trajectory prediction for the target vehicle based on the data 1502, 1504 for the target vehicle and conditioned on the candidate trajectory 1400. In an embodiment, the model 306 outputs the predicted responses 1500 in the form of data representing predicted trajectories by the target vehicle (including a probability of each trajectory). Input data for multiple target vehicles or candidate trajectories, or both, can be provided to the model 1506 in parallel to obtain predicted responses 1500 by multiple vehicles to each candidate trajectory 1400 in the set of candidate trajectories. Additional details regarding the model 1506 and its inputs and outputs are described in Phan-Minh, Tung, et al. “CoverNet: Multimodal Behavior Prediction Using Trajectory Sets.” ArXiv:1911.10298, the entire contents of which is incorporated herein by reference.

In an embodiment, the conditional motion predictor 1306 filters the target vehicles for which responses 1500 are predicted. For example, the conditional motion predictor 1306 filters the target vehicles to include only those target vehicles within a threshold distance (e.g., 10 meters) of the vehicle at all times during the candidate trajectory, or only those target vehicles within threshold distance (e.g., 20 meters) of the vehicle at some point during the candidate trajectory, or both. In an embodiment, the conditional motional predictor 1306 filters the target vehicles to include only those target vehicles within a threshold distance and direction of the vehicle (e.g., 20 meters in front of the vehicle) at some or all times during the candidate trajectory. By filtering out target vehicles that do not significantly impact the trajectory of the vehicle, the conditional motion predictor 1306 reduces the consumption of computational resources during prediction without a significant loss in quality.

Referring to FIG. 16 , the predicted responses 1500 and their corresponding probabilities are provided to the trajectory selector 1308. In general, the trajectory selector 1308 assigns a score to each candidate trajectory 1400 based on the predicted responses 1500, the probabilities of the predicted responses, and characteristics of the trajectory, among others. The trajectory selector 1308 then selects a candidate trajectory 1600 from the set of candidate trajectories 1400 for the vehicle based on the score (e.g., the highest or lowest scoring candidate trajectory) and provides data representing the selected trajectory 1604 to a control module 1314 (e.g., the control module 406) to cause the vehicle to travel along the trajectory. In an embodiment, the trajectory selector 1308 selects a candidate trajectory 1600 from the set of candidate trajectories 1400 according to a set of rules (e.g., a rule book) as described in U.S. patent application Ser. No. 16/996,785, titled “Data Driven Rule Books,” the entire content of which is incorporated herein by reference.

In an embodiment, the trajectory selector 1308 calculates a score for the candidate trajectory based on a safety score 1602 and one or more heuristics 1604 for the predicted responses or the candidate trajectory, or both. For example, the trajectory selector 1308 calculates a safety score 1602 for the candidate trajectory or the predicted response, or both, based on whether the candidate trajectory or predicted response would cause the vehicle or the target vehicle to crash, experience a near-crash, engage in harsh braking or acceleration, move at a high or low speed, or combinations of them, among others. In an embodiment, a near-crash includes a situation in which the candidate trajectory or the predicted response would cause the vehicle or the target vehicle to come within a threshold distance (e.g., about 0.5 meters, about 1 meter, etc.) of each other or another object, would result in a crash but for an evasive maneuver by the vehicle or the target vehicle (or a driver of the vehicle or the target vehicle), or would cause another unsafe condition at the vehicle or the target vehicle, such as one or both of the vehicles driving off the road. In an embodiment, determining whether the candidate trajectory or the predicted response would cause the vehicle or the target vehicle to engage in harsh braking or acceleration or move at high or low speeds includes determining whether a magnitude of acceleration (or deceleration) or speed at the vehicle or the target vehicle exceeds (or falls below) a threshold. In an embodiment, the threshold accounts for characteristics of the vehicle (e.g., a type of the vehicle, such as a truck or a sports car) or the environment (e.g., weather, speed limit), or both. In an embodiment, the trajectory selector 1308 considers the future of the vehicle or the target vehicle when calculating the safety score 1602, such as a time-to-collision or a distance-to-collision at the end (or another point) of the trajectory or predicted response. For example, the trajectory selector 1308 assigns a worse score to a trajectory or predicted response that leaves the vehicle or the target vehicle with less time or distance before a collision at the end of the trajectory or predicted response.

The trajectory selector 1308 also calculates one or more heuristics 1604 for the candidate trajectory or the predicted response, or both, such as an amount of distance traveled over the trajectory or predicted response (e.g., predicted trajectory). In an embodiment, the trajectory selector 1308 assigns a better (e.g., higher or lower) score to a candidate trajectory having a greater distance traveled or eliciting a response having a greater distance traveled. In an embodiment, weightings are applied to the safety score 1602 and the heuristics 1604 when determining an overall score for the candidate trajectory. For example, the trajectory selector 1308 assigns a greater weight to the safety score 1602 to ensure that the safest candidate trajectory is selected. As another example, the trajectory selector 1308 assigns a greater weight to the heuristics 1604 to give preference to trajectories that maximize distance traveled.

FIG. 17 shows a flowchart of an example process 1700 for conditional motion prediction. In an embodiment, the vehicle is the AV 100 shown in FIG. 1 , and the process 1700 is carried out by a processor, such as the processor 304 shown in FIG. 3 .

The processor generates 1702 a set of candidate trajectories for the vehicle based on possible macro actions by the vehicle. In an embodiment, the set of candidate trajectories include the candidate trajectories 1400 shown in FIG. 14 . In an embodiment, the possible macro actions include the macro actions 1402 parameterized with the parameters 1404 shown in FIG. 14 . In an embodiment, the possible macro actions include a lane follow action have a parameterized velocity or a change lane action having a parameterized lane change time, or both. In an embodiment, the processor identifies a maneuver (e.g., merge, lane change, turn) to be performed by the vehicle on a road network and generates the set of candidate trajectories for performing the maneuver on the road network.

For at least some trajectories in the set of candidate trajectories, the processor predicts 1704 a response by a target vehicle to the respective trajectory and a probability of the response by the target vehicle. In an embodiment, the predicted response is one of the predicted responses 1500 shown in FIG. 15 . In an embodiment, predicting the response of the target vehicle to the trajectory includes predicting a trajectory of the target vehicle conditioned on the trajectory of the vehicle (e.g., through application of a deep learning model, such as the model 1506 shown in FIG. 15 ).

The processor selects 1706 a trajectory from the set of candidate trajectories based at least in part on the predicted response by the target vehicle to the trajectory, the probability of the response by the target vehicle, and characteristics of the trajectory. In an embodiment, the selected trajectory is the selected trajectory 1600 shown in FIG. 16 . In an embodiment, selecting the trajectory from the set of candidate trajectories includes determining a score for each trajectory in the set of candidate trajectories based at least in part on the response by the target vehicle to the trajectory, the probability of the response by the target vehicle, and the characteristics of the trajectory, and selecting the trajectory from the set of candidate trajectories based on the score. In an embodiment, determining the score includes, for example, identifying a magnitude of acceleration by the vehicle for the trajectory or by the target vehicle in response to the trajectory, identifying a collision or near-collision by the vehicle for the trajectory or by the target vehicle in response to the trajectory, determining a time-to-collision or distance-to-collision for the vehicle for the trajectory or for the target vehicle in response to the trajectory, or determining a distance traveled by the vehicle for the trajectory or by the target vehicle in response to the trajectory, or combinations of them, among others. In an embodiment, the score is determined based on the safety score 1602 or one or more heuristics 1604, or both, for the candidate trajectory or the predicted response, or both.

In an embodiment, the target vehicle is a first target vehicle, and for each trajectory in the set of candidate trajectories, the processor predicts a response by a second, different target vehicle (e.g., another target vehicle in the vicinity/trajectory of the vehicle) to the trajectory and a probability of the response by the second target vehicle. The processor selects the trajectory from the set of candidate trajectories based at least in part on the response by the first target vehicle to the trajectory, the probability of the response by the first target vehicle, the response by the second target vehicle to the trajectory, the probability of the response by the second target vehicle, and the characteristics of the trajectory.

After selecting the trajectory from the set of candidate trajectories, the processor operates 1708 (or causes to operate) the vehicle based on the selected trajectory.

In the foregoing description, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The description and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further comprising,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step/sub-entity of a previously-recited step or entity. 

What is claimed is:
 1. A vehicle, comprising: a computer-readable media storing computer-executable instructions; and a processor communicatively coupled to the computer-readable media, the processor configured to execute the computer executable instructions to perform operations comprising: generating a set of candidate trajectories for the vehicle based on possible macro actions by the vehicle; for at least some trajectories in the set of candidate trajectories, predicting a response by a target vehicle to a respective trajectory and a probability of the response by the target vehicle; and determining a score for the respective trajectory based on a weighted combination of: (i) the predicted response by the target vehicle to the respective trajectory and the probability of the response by the target vehicle, and (ii) at least one heuristic calculated for the respective trajectory, wherein calculating the at least one heuristic for the respective trajectory comprises determining a distance traveled for the respective trajectory, and wherein a higher score is assigned to a trajectory having a greater distance traveled relative to distances of other trajectories in the set of candidate trajectories; selecting a trajectory from the set of candidate trajectories based at least in part on the score; and operating the vehicle based on the selected trajectory.
 2. The vehicle of claim 1, further comprising: identifying a maneuver to be performed by the vehicle on a road network; and generating the set of candidate trajectories for performing the maneuver on the road network.
 3. The vehicle of claim 2, wherein the maneuver comprises at least one of a merge, a lane change, or a turn.
 4. The vehicle of claim 1, wherein the possible macro actions comprise a lane follow action having a parameterized velocity.
 5. The vehicle of claim 1, wherein the possible macro actions comprise a change lane action having a parameterized lane change time.
 6. The vehicle of claim 1, wherein predicting the response of the target vehicle to the trajectory comprises predicting a trajectory of the target vehicle conditioned on the trajectory of the vehicle.
 7. The vehicle of claim 1, wherein determining the score comprises identifying a magnitude of acceleration by the vehicle for the trajectory or by the target vehicle in response to the trajectory.
 8. The vehicle of claim 1, wherein determining the score comprises identifying a collision or near-collision by the vehicle for the trajectory or by the target vehicle in response to the trajectory.
 9. The vehicle of claim 1, wherein determining the score comprises determining a time-to-collision or distance-to-collision for the vehicle for the trajectory or for the target vehicle in response to the trajectory.
 10. The vehicle of claim 1, wherein determining the score comprises determining a distance traveled by the vehicle for the trajectory or by the target vehicle in response to the trajectory.
 11. The vehicle of claim 1, wherein the target vehicle comprises a first target vehicle, the processor configured to execute the computer executable instructions to perform operations further comprising: for each trajectory in the set of candidate trajectories, predicting a response by a second target vehicle to the trajectory and a probability of the response by the second target vehicle, wherein the second target vehicle is different than the first target vehicle.
 12. The vehicle of claim 11, wherein the processor is configured to execute the computer executable instructions to perform operations further comprising: selecting the trajectory from the set of candidate trajectories based at least in part on the response by the first target vehicle to the trajectory, the probability of the response by the first target vehicle, the response by the second target vehicle to the trajectory, the probability of the response by the second target vehicle, and characteristics of the trajectory.
 13. A method comprising: generating, by at least one processor, a set of candidate trajectories for a vehicle based on possible macro actions by the vehicle; for at least some trajectories in the set of candidate trajectories, predicting, by the at least one processor, a response by a target vehicle to a respective trajectory and a probability of the response by the target vehicle; and determining a score for the respective trajectory based on a weighted combination of: (i) the predicted response by the target vehicle to the respective trajectory and the probability of the response by the target vehicle, and (ii) at least one heuristic calculated for the respective trajectory, wherein calculating the at least one heuristic for the respective trajectory comprises determining a distance traveled for the respective trajectory, and wherein a higher score is assigned to a trajectory having a greater distance traveled relative to distances of other trajectories in the set of candidate trajectories; selecting, by the at least one processor, a trajectory from the set of candidate trajectories based at least in part on the score; and operating, by the at least one processor, the vehicle based on the selected trajectory.
 14. The method of claim 13, further comprising: identifying a maneuver to be performed by the vehicle on a road network; and generating the set of candidate trajectories for performing the maneuver on the road network.
 15. The method of claim 14, wherein the maneuver comprises at least one of a merge, a lane change, or a turn.
 16. The method of claim 13, wherein the possible macro actions comprise at least one of a lane follow action having a parameterized velocity or a change lane action having a parameterized lane change time.
 17. The method of claim 13, wherein predicting the response of the target vehicle to the trajectory comprises predicting a trajectory of the target vehicle conditioned on the trajectory of the vehicle.
 18. The method of claim 13, wherein determining the score comprises: identifying at least one of: a magnitude of acceleration by the vehicle for the trajectory or by the target vehicle in response to the trajectory, a collision or near-collision by the vehicle for the trajectory or by the target vehicle in response to the trajectory, a time-to-collision or distance-to-collision for the vehicle for the trajectory or for the target vehicle in response to the trajectory, or a distance traveled by the vehicle for the trajectory or by the target vehicle in response to the trajectory; and determining the score based on the identification of the at least one of: the magnitude of acceleration by the vehicle for the trajectory or by the target vehicle in response to the trajectory, the collision or near-collision by the vehicle for the trajectory or by the target vehicle in response to the trajectory, the time-to-collision or distance-to-collision for the vehicle for the trajectory or for the target vehicle in response to the trajectory, or the distance traveled by the vehicle for the trajectory or by the target vehicle in response to the trajectory.
 19. The method of claim 13, wherein the target vehicle comprises a first target vehicle, the method further comprising: for each trajectory in the set of candidate trajectories, predicting a response by a second target vehicle to the trajectory and a probability of the response by the second target vehicle, wherein the second target vehicle is different than the first target vehicle.
 20. The method of claim 19, further comprising: selecting the trajectory from the set of candidate trajectories based at least in part on the predicted response by the first target vehicle to the trajectory and the probability of the response by the first target vehicle, the predicted response by the second target vehicle to the trajectory and the probability of the response by the second target vehicle, and characteristics of the trajectory.
 21. At least one non-transitory computer-readable storage media comprising instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising: generating a set of candidate trajectories for a vehicle based on possible macro actions by the vehicle; for at least some trajectories in the set of candidate trajectories, predicting a response by a target vehicle to a respective trajectory and a probability of the response by the target vehicle; and determining a score for the respective trajectory based on a weighted combination of: (i) the predicted response by the target vehicle to the respective trajectory and the probability of the response by the target vehicle, and (ii) at least one heuristic calculated for the respective trajectory, wherein calculating the at least one heuristic for the respective trajectory comprises determining a distance traveled for the respective trajectory, and wherein a higher score is assigned to a trajectory having a greater distance traveled relative to distances of other trajectories in the set of candidate trajectories; selecting a trajectory from the set of candidate trajectories based at least in part on the score; and operating the vehicle based on the selected trajectory.
 22. The at least one non-transitory computer-readable storage media of claim 21 storing instruction which, when executed by the at least one processor, cause the at least one processor to perform operations further comprising: identifying a maneuver to be performed by the vehicle on a road network; and generating the set of candidate trajectories for performing the maneuver on the road network.
 23. The at least one non-transitory computer-readable storage media of claim 22, wherein the maneuver comprises at least one of a merge, a lane change, or a turn.
 24. The at least one non-transitory computer-readable storage media of claim 21, wherein the possible macro actions comprise at least one of a lane follow action having a parameterized velocity or a change lane action having a parameterized lane change time.
 25. The at least one non-transitory computer-readable storage media of claim 21, wherein predicting the response of the target vehicle to the trajectory comprises predicting a trajectory of the target vehicle conditioned on the trajectory of the vehicle.
 26. The at least one non-transitory computer-readable storage media of claim 21, wherein determining the score comprises identifying at least one of: a magnitude of acceleration by the vehicle for the trajectory or by the target vehicle in response to the trajectory, a collision or near-collision by the vehicle for the trajectory or by the target vehicle in response to the trajectory, a time-to-collision or distance-to-collision for the vehicle for the trajectory or for the target vehicle in response to the trajectory, or a distance traveled by the vehicle for the trajectory or by the target vehicle in response to the trajectory.
 27. The at least one non-transitory computer-readable storage media of claim 21, wherein the target vehicle comprises a first target vehicle, and wherein the at least one non-transitory computer-readable storage media storing instruction which, when executed by the at least one processor, cause the at least one processor to perform operations further comprising: for each trajectory in the set of candidate trajectories, predicting a response by a second target vehicle to the trajectory and a probability of the response by the second target vehicle, wherein the second target vehicle is different than the first target vehicle; and selecting the trajectory from the set of candidate trajectories based at least in part on the predicted response by the first target vehicle to the trajectory and the probability of the response by the first target vehicle, the predicted response by the second target vehicle to the trajectory and the probability of the response by the second target vehicle, and characteristics of the trajectory. 